This paper addresses the notion that many fractional I(d) processes may fall into the "empty box" category, as discussed in Granger (1999). We begin by showing that so-called spurious long memory may arise not only in the presence of (stochastic) structural breaks and regime switches, but also when the true data generating processes (DGPs) are linear with no structural breaks, and/or regime switching properties. However, we also present ex ante forecasting evidence based on an updated version of the absolute returns series examined by Ding, Granger and Engle (1993) that suggests that ARFIMA models estimated using a variety of standard estimation procedures yield "approximations" to the true unknown underlying DGP that provide significantly better out-of-sample predictions than AR, MA, and random walk models, with no models being "better" than ARFIMA models, based on analysis of point mean square forecast errors (MSFEs) as well as using Diebold and Mariano (DM: 1995) predictive accuracy tests. We also provide ex ante prediction evidence that 130 of the 215 macroeconomic variables used in Stock and Watson (SW: 2002) have lower (or equal) point MSFEs for ARFIMA-best models than for a large class of non-ARFIMA models. This result is obtained even though our ex-ante forecasting experiments using these variables involve prediction periods of only 200-250 months, and even though there is clear and substantial evidence of severe bias in various in-sample estimates of the differencing parameter. Furthermore, when a new prediction based estimator of d that we introduce is used to construct ARFIMA models for a subset of the SW dataset, many point MSFE error improvements associated with the use of ARFIMA models also become significant. These findings, taken together with the results of an empirical analysis of stock returns for 5 different countries and an extensive Monte Carlo analysis of a variety of different estimation and testing methods lead us to conclude that ARFIMA models might not fall into the "empty box" category after all, although much further research is needed before conclusive evidence in either direction can be given.JEL classification: C15, C22, C53.

Investors face significant barriers in evaluating the performance of hedge funds and commodity trading advisors (CTAs). The only available performance data comes from voluntary reporting to private companies. Funds have incentives to strategically report to these companies, causing these data sets to be severely biased. And, because hedge funds use nonlinear, state-dependent, leveraged strategies, it has proven difficult to determine whether they add value relative to benchmarks. We focus on commodity trading advisors, a subset of hedge funds, and show that during the period 1994-2007 CTA excess returns to investors (i.e., net of fees) averaged 85 basis points per annum over US T-bills, which is insignificantly different from zero. We estimate that CTAs on average earned gross excess returns (i.e., before fees) of 5.4%, which implies that funds captured most of their performance through charging fees. Yet, even before fees we find that CTAs display no alpha relative to simple futures strategies that are in the public domain. We argue that CTAs appear to persist as an asset class despite their poor performance, because they face no market discipline based on credible information. Our evidence suggests that investors' experience of poor performance is not common knowledge.

Gorton and Rouwenhorst (2006) examined commodity futures returns over the period July 1959 to December 2004 based on an equally-weighted index. They found that fully collateralized commodity futures had historically offered the same return and Sharpe ratio as U.S. equities, but were negatively correlated with the return on stocks and bonds. Reviewing these results ten years later, we find that our conclusions largely hold up out-of-sample. The in-and out-of-sample average commodity risk premiums are not significantly different, nor is the cross-sectional relationship between average returns and the basis. Correlations among commodities and commodity correlations with other assets experienced a temporary increase during the financial crisis which is in line with historical experience of variation of these correlations over the business cycle.

, and seminar participants at UMass Amherst. The views expressed are those of the authors and do not necessarily reflect the official position of AIG Financial Products Corp or the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

has nothing to currently disclose. He was a consultant to AIG Financial Products from 1996-2008. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. At least one co-author has disclosed a financial relationship of potential relevance for this research. Further information is available online at http://www.nber.org/papers/w21243.ack NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.